Künstliche Intelligenz Ingenieur

Syn2core; München; On-site; EUR 90,000 - 140,000; Full time

Job summary

Syn2core is building ML-enabled copilots for industrial settings, delivering robust models on production lines with hidden data privacy considerations. You will own end-to-end model development, from data to deployment, in collaboration with the CTO and Lead ML Engineer.

The role focuses on proving model reliability, data scarcity solutions, and edge deployment across automotive manufacturing environments, with a strong emphasis on reproducibility and real-world impact.

Qualifications

• Strong PyTorch and production ML experience in detection, classification, or tracking.
• SLAM/spatial perception used in production.
• Model optimization for edge hardware (ONNX, TensorRT).

Responsibilities

• Build end-to-end training pipelines and model registry with lineage.
• Develop synthetic data generation for rare anomalies.
• Edge deployment: productionize audio/vision and SLAM vehicle IDs.
• Optimize resources for constrained factory hardware.

Skills

PyTorch ML

SLAM

Edge HW

Tools

ONNX

TensorRT

Job description

At Syn2core, we're building AI Copilot, an AI assistant that runs on smart glasses to support assembly-line workers in real time. Our system streams and processes data (video, audio, IMU sensor data) in real time, and delivers audio feedback directly to the worker. We're deploying it on the production lines of the automotive industry across the world — privacy-first by design: everything we persist is anonymized at ingestion.

Our long-term vision: the egocentric data we collect is training fuel for the next generation of robot learning – positioning us at the heart of the race towards autonomous robots for industrial deployment in automotive, aviation, aerospace and beyond.

The role

Our clients accept our system through formal tests with hard recall and false-positive gates — so our models must provably work: which weights, trained on which data, with which config, always answerable. You will own that machinery end-to-end, and with it two of our hardest ML problems: synthetic data generation for rare anomalies, and spatial reasoning — knowing which vehicle a worker is acting on as they move between cars, using SLAM fused with vehicle identity signals.

You'll work as a peer of our Lead ML Engineer — they own what the system should do, you own how models get built, trained, and reproduced — designing together, in the open, with a direct line to the CTO.

What you'll do

• Build the machinery that makes models provable — training pipelines, experiment tracking, model registry: full lineage from dataset to deployed weights, plus the evaluation harnesses we stake client acceptance on
• Solve data scarcity — simulation-based synthetic data pipelines for anomaly classes real factories are too good to produce often
• Take the system beyond vision — productionize our audio modality; develop worker–vehicle association with SLAM and vehicle identity signals
• Ship at the edge — own the anonymization models our privacy guarantees depend on; optimize everything for constrained GPU/CPU budgets on factory hardware

What you get

• The ML production culture of a company, shaped by you from the start — registry, tracking, evals, your way
• Multimodal problems (vision, audio, spatial) most teams only get one of, on data nobody else has
• Your models on real assembly lines at major OEMs within weeks, with measurable stakes

Where you'll be in 12 months

Every model that passes client acceptance is reproducible from the registry. A rare-anomaly class hit its recall gate on synthetic data. Audio is live in a deployment, and SLAM-based worker–vehicle association is validated on a real line. We'll get there together — the architecture with our Lead ML Engineer, the machinery yours.

Who you are

• You report the real number, especially when it's bad — our clients' acceptance tests leave no room for flattering evals
• You build machines that build models: reproducibility over heroics
• You prefer solving a problem once, generally, over solving it five times quickly
• You explore broadly, then converge and commit
• You're creative about data scarcity — synthesis, augmentation, simulation

Your experience

Must have:

• Strong PyTorch and production ML experience (detection / classification / tracking)
• SLAM / spatial perception used in production, not just coursework
• Model optimization for edge hardware (e.g. ONNX, TensorRT)

Ways to stand out:

• Manufacturing, robotics, or other physical-world domains